Neuralink, the Elon Musk-founded company developing implantable chips that can read brain waves, has raised an additional $43 million in venture capital, according to a filing with the SEC.
The filing published this week shows the company increased its previous tranche, led by Peter Thiel’s Founders Fund, from $280 million to $323 million in early August. Thirty-two investors participated, according to the filing.
Neuralink hasn’t disclosed its valuation recently. But in June, Reuters reported that the company was valued at about $5 billion after privately-executed stock trades.
“Luna is a twenty-first-century penal colony but, since no one can stand Earth gravity after being on the moon for a few weeks, all who are sent there must stay. When the liberated people rise against the authority, they receive unexpected help from a computer with a personality.“ Part 1 ||||| You can find Part 2 here: https://youtu.be/P1jI2Oh4-lo. Chapter list: 00:00:00 — (i) Book info. 00:02:25 — (01) That Dinkum Thnkum 01 00:27:06 — (02) That Dinkum Thnkum 02 00:57:20 — (03) That Dinkum Thnkum 03 01:35:45 — (04) That Dinkum Thnkum 04 02:03:08 — (05) That Dinkum Thnkum 05 02:34:06 — (06) That Dinkum Thnkum 06 03:09:22 — (07) That Dinkum Thnkum 07 03:30:53 — (08) That Dinkum Thnkum 08 03:49:45 — (09) That Dinkum Thnkum 09 04:46:19 — (10) That Dinkum Thnkum 10 05:12:25 — (11) That Dinkum Thnkum 11 05:47:51 — (12) That Dinkum Thnkum 12 06:08:50 — (13) That Dinkum Thnkum 13
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My name is Artem, I’m a computational neuroscience student and researcher. In this video we discuss engrams – fundamental units of memory in the brain. We explore what engrams are, how memory is allocated, where it is stored, and how different memories become linked with each other.
OUTLINE: 00:00 — Introduction. 00:39 — Historical background. 01:44 — Fear conditioning paradigm. 03:38 — Immediate-early genes as memory markers. 08:13 — Engrams are necessary and sufficient for recall. 10:16 — Excitabiliy and memory allocation. 16:19 — Brain-wide engrams. 18:12 — Linking memories together. 24:20 — Summary. 25:33 — Brilliant. 27:09 — Outro.
REFERENCES (in no particular order): 1. Robins, S. The 21st century engram. WIRES Cognitive Science e1653 (2023) doi:10.1002/wcs.1653. 2. Roy, D. S. et al. Brain-wide mapping reveals that engrams for a single memory are distributed across multiple brain regions. Nat Commun 13, 1799 (2022). 3. Josselyn, S. A. & Tonegawa, S. Memory engrams: Recalling the past and imagining the future. Science 367, eaaw4325 (2020). 4. Chen, L. et al. The role of intrinsic excitability in the evolution of memory: Significance in memory allocation, consolidation, and updating. Neurobiology of Learning and Memory 173, 107266 (2020). 5. Rao-Ruiz, P., Yu, J., Yu, J. J., Kushner, S. A. & Josselyn, S. A. Neuronal competition: microcircuit mechanisms define the sparsity of the engram. Current Opinion in Neurobiology 54163–170 (2019). 6. Josselyn, S. A. & Frankland, P. W. Memory Allocation: Mechanisms and Function. Annu. Rev. Neurosci. 41389–413 (2018). 7. Choi, J.-H. et al. Interregional synaptic maps among engram cells underlie memory formation. Science 360430–435 (2018). 8. Abdou, K. et al. Synapse-specific representation of the identity of overlapping memory engrams. Science 360, 1227–1231 (2018). 9. Yokose, J. et al. Overlapping memory trace indispensable for linking, but not recalling, individual memories. Science 355398–403 (2017). 10. Rashid, A. J. et al. Competition between engrams influences fear memory formation and recall. Science 353383–387 (2016). 11. Poo, M. et al. What is memory? The present state of the engram. BMC Biol 14, 40 (2016). 12. Park, S. et al. Neuronal Allocation to a Hippocampal Engram. Neuropsychopharmacol 41, 2987–2993 (2016). 13. Morrison, D. J. et al. Parvalbumin interneurons constrain the size of the lateral amygdala engram. Neurobiology of Learning and Memory 135, 91–99 (2016). 14. Minatohara, K., Akiyoshi, M. & Okuno, H. Role of Immediate-Early Genes in Synaptic Plasticity and Neuronal Ensembles Underlying the Memory Trace. Front. Mol. Neurosci. 8, (2016). 15. Josselyn, S. A., Köhler, S. & Frankland, P. W. Finding the engram. Nat Rev Neurosci 16521–534 (2015). 16. Yiu, A. P. et al. Neurons Are Recruited to a Memory Trace Based on Relative Neuronal Excitability Immediately before Training. Neuron 83722–735 (2014). 17. Redondo, R. L. et al. Bidirectional switch of the valence associated with a hippocampal contextual memory engram. Nature 513426–430 (2014). 18. Ramirez, S. et al. Creating a False Memory in the Hippocampus. Science 341387–391 (2013). 19. Liu, X. et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature 484381–385 (2012). 20. Silva, A. J., Zhou, Y., Rogerson, T., Shobe, J. & Balaji, J. Molecular and Cellular Approaches to Memory Allocation in Neural Circuits. Science 326391–395 (2009).
The Sirius 16 is perhaps the first gaming laptop to ship with Linux, equipped with impressive hardware from AMD’s latest mobile Ryzen CPU and Radeon GPU.
To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov/ The first 200 of you will get 20% off Brilliant’s annual premium subscription.
My name is Artem, I’m a computational neuroscience student and researcher. In this video we discuss the Tolman-Eichenbaum Machine – a computational model of a hippocampal formation, which unifies memory and spatial navigation under a common framework.
OUTLINE: 00:00 — Introduction. 01:13 — Motivation: Agents, Rewards and Actions. 03:17 — Prediction Problem. 05:58 — Model architecture. 06:46 — Position module. 07:40 — Memory module. 08:57 — Running TEM step-by-step. 11:37 — Model performance. 13:33 — Cellular representations. 17:48 — TEM predicts remapping laws. 19:37 — Recap and Acknowledgments. 20:53 — TEM as a Transformer network. 21:55 — Brilliant. 23:19 — Outro.
REFERENCES: 1. Whittington, J. C. R. et al. The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation. Cell 183, 1249–1263.e23 (2020). 2. Whittington, J. C. R., Warren, J. & Behrens, T. E. J. Relating transformers to models and neural representations of the hippocampal formation. Preprint at http://arxiv.org/abs/2112.04035 (2022). 3. Whittington, J. C. R., McCaffary, D., Bakermans, J. J. W. & Behrens, T. E. J. How to build a cognitive map. Nat Neurosci 25, 1257–1272 (2022).
Wouldn’t it be nice to have a computer answer all of the biggest questions in the universe?
In his first year of graduate school, in 2013, Michael Wagman walked into his advisor’s office and asked, “Can you help me simulate the universe?”
Wagman, a theoretical physicist and associate scientist at the US Department of Energy’sFermi National Accelerator Laboratory, thought it seemed like a reasonable question to ask. “We have all of these beautiful theoretical descriptions of how we think the world works, so I wanted to try and connect those formal laws of physics to my everyday experience of reality,” he says.
As connectivity continues to expand and the number of devices on a network with it, IoT’s ambition of creating a world of connected things grows. Yet, with pros, comes the cons, and the flip side of this is the growing security challenges that come with it too.
Security has been a perennial concern for IoT since it’s utilisation beyond its use for basic functions like tallying the stock levels of a soda machine. However, for something of such interest to the industry, plans for standardisation remain allusive. Instead, piece meal plans to ensure different elements of security, like zero trust for identity and access management for devices on a network, or network segmentation for containing breaches, are undertaken by different companies according to their needs.
Yet with the advancement of technology, things like quantum computing pose a risk to classic cryptography methods which, among other things, ensures data privacy is secure when being transferred from device to device or even to the Cloud.
Transistors are crucial electronic components that regulate, amplify and control the flow of current inside most existing devices. In recent years, electronics engineers have been trying to identify materials and design strategies that could help to further improve the performance of transistors, while also reducing their size.
Two-dimensional (2D) transition metal dichalcogenides have some advantageous properties that could help to enhance the capabilities of transistors. While past studies have demonstrated the potential of these materials in individual transistors, their use for developing entire integrated circuits (ICs) that operate at high frequencies has proved challenging.
Researchers at Nanjing University in China recently created new ICs that can operate at GHz frequencies, based on the 2D semiconducting material monolayer molybdenum disulfide (MoS2). Their devices, presented in a Nature Electronics paper, rely on MoS2-based field-effect transistors (FETs).
Quantum advantage is the milestone the field of quantum computing is fervently working toward, where a quantum computer can solve problems that are beyond the reach of the most powerful non-quantum, or classical, computers.
Quantum refers to the scale of atoms and molecules where the laws of physics as we experience them break down and a different, counterintuitive set of laws apply. Quantum computers take advantage of these strange behaviors to solve problems.
There are some types of problems that are impractical for classical computers to solve, such as cracking state-of-the-art encryption algorithms. Research in recent decades has shown that quantum computers have the potential to solve some of these problems. If a quantum computer can be built that actually does solve one of these problems, it will have demonstrated quantum advantage.
In 1960, DARPA funded three university-based Inderdisciplinary Laboratories (IDLs) that opened the way toward an enormous field of research and development known today as materials science and engineering. In this video, DARPA program managers, DARPA-funded researchers, and a Naval Research Laboratory scientist tell this field-building story as it unfolded over the past six decades, all the while delivering breakthroughs in the way materials are designed, processed, and deployed to push technologies forward. Intelligent processing of materials (IPM), accelerated insertion of materials (AIM), and integrated computational materials engineering (ICME) are among the specific programs detailed in the video. DARPA is currently developing technologies that enable the crafting of new materials with unprecedented properties by designing and controlling matter from atoms on up to human-scale systems.
